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Javier Toret: methodological reflections

It is important to stress the different approach of this research: instead of an ex-ante design and hypotheses, the huge amount of data allow for a reverse conception: see what are the patterns that arise and, after them, infer the hypotheses.

The 15M means that a technological and social critical mass takes the street: a long history of movements, unrests and protests finally crystallize as a major protest and camps all over Spain. The profile, though, is not the usual profile of a social movement, but of a network movement: there are several sub-movements in action, several hashtags and memes, several proposals, etc.

A working hypothesis is that as the network movement grows, the interest and participation in “real” politics also grows. Technopolitics is neither slacktivism nor cyberactivism: the goal is real politics and “real life”. Technopolitics is a tactical and strategic use of digital tools and collective identities. The aim of technopolitics is to organize, communicate and act.

Technopolitics have a certain sense of forecasting: they anticipate what is going to happen, or what is about to happen, and help it in finally making it happen, catalysing the change. Technolpolitics drive the flow of the collective action.

Technopolitics, though, heavily rely on technology, in two ways: (1) people intensively use technology to inform and be informed, to coordinate and organize, but also (2) online participation counts as 100% participation, it is not a second best but simply another channel for participation and engagement. Technopolitics normalizes the use of technology.

Another working hypothesis is that the Arab Spring was a reference for the 15M, and the demonstrations on Tahrir Square were key for AcampadaSol (the camps initially in Madrid Puerta del Sol square and after in the rest of Spanish squares).

Alberto Lumbreras: tools

Analysis is done by following the movements of hashtagsin Twitter, including the ‘flocks’ or ‘swarms’ of Twitter users. And thus be able to tell the political relationship between hashtags, users, etc.

To analyse flocks one can either follow the hashtag in real time or recovering data (e.g. from Topsy), analyse what users are following or tweeting two different (but related) hashtags, and then analyze them with a social network analysis software (e.g. Gephi).

This flock analysis allows testing (1) whether the 15M was the product of prior citizen movements in Spain and (2) whether it had any relationship with the Arab Spring. For instance, 31% of the users that tweeted under #spanishrevolution had already twitted #nolesvotes (the movement against bipartidism in Spain). And what also happened is that #spanishrevolution brought back to life #nolesvotes [disclaimer: data are not still very accurate].

Issues: Topsy provides a truncated and thus biased sample of the tweets; we do not know how big has a flock to be to e considered as the generator/influencer of a movement.

Democracia Real Ya was able to activate and engage many existing platforms and groups that had either been very active in citizen protests/demands or were planning to be or wanted to but did not know how (e.g. how to create a critical mass and be relevant).

15M was active in 59 cities through 59 local groups: the explosion of the #15 as a big event/movement turned itself into a massive creation of local camps and local groups connected at a national level, but acting somewhat individually/locally. The affective commotion fostered a distributed and self-organized movement; and the viral propagation was key for the local nodes to be able to be effective.

But, what happened between the 15M until the 22M so that the phenomenon boosted the way it did?

The growth of profiles follows a pattern of simple and logical self-organization fostered by technology. Attention is synced around some very specific issues (e.g. nobody searches “democracy” in Google in Spain… until May 2011, when it peaks!). The 15M can be understood as an event, an augmented event, interconnected and that affects people whether they are present in the physical space or not.

The 15M fosters a cognitive diet: instant messaging, blogging, usage of social networking sites, etc. are intensively used in search of information and communication channels, in search of knowing, in search of understanding.

It is important to note the importance of the subjective/emotional factor of the 15M. The 15M enters the emotions of people and this is shown by what people tweeted those days. There is a need for an emotional analysis of the 15M as it will contribute to explain how it worked and spread.

Óscar Marín: emotions in Twitter

To be able to analyze emotions in Twitter some questions have to be made in order to establish an ontology: what are the predominant emotions in the 15M, what is the emotional charge, what is the relationship between the emotional charge and virality. After these questions, a list of expressions is created that identifies emotions, a grammar is used to detect negations, and a corpus of twitts is tagged manually so that the analysis can be iterated.

What data show is that there is more emotional virality (vs. non-emotional virality) when people are physically together. Or, in other words, the virality of emotional tweets is not unconditionally superior to non-emotional virality, but it depends on people being physically together. We can check this for instance by seeing that emotional charge is much bigger around May 15th 2011 (previous day and a few following days) than on any other date in the time-span of the movement.

Physical events, and the emotions of “empowerment” and “indignation” are they keys to understand the emotional factor in the 15M movement.

Related to emotions, some questions can be also put about the vocabulary: does it evolve, does it have anything to do with virality, what is the frequency (temperature) of a given term, is the vocabulary used coherent, etc.

Data show that, initially, terms rotate with certain speed and that they are weakly related one with each other (low cohesion). As time advances, the vocabulary has much more cohesion, becomes more restricted (less words) and more stable (remain longer in time): the message becomes clearer and stronger. Last, as the core event (camps) fades away, so does the vocabulary, that again has lower cohesion and higher rotation.

Discussion

Q: It would be in interesting outcome of emotional and vocabulary analysis the finding of outliers.

[the session goes on in a second part, which I cannot attend :( ]

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Digital culture, networks and distributed politics in the age of the Internet (2012)